Cloudera CDP-3002 exam dumps : CDP Data Engineer - Certification Exam

  • Exam Code: CDP-3002
  • Exam Name: CDP Data Engineer - Certification Exam
  • Updated: Sep 13, 2026     Q & A: 320 Questions and Answers

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Cloudera CDP-3002 Exam Syllabus Topics:

SectionObjectives
Topic 1: Data Governance and Security- Security frameworks
  • 1. Authentication and authorization in CDP
    • 2. Ranger / Sentry policies
      - Data governance
      • 1. Metadata management
        • 2. Data lineage and auditing
          Topic 2: Platform Operations- Cluster and workload management
          • 1. Monitoring and troubleshooting
            • 2. Resource management and scheduling
              Topic 3: Data Storage and Modeling- Data modeling
              • 1. Schema design for analytics
                • 2. Dimensional modeling concepts
                  - Data lake architecture
                  • 1. HDFS / cloud object storage concepts
                    • 2. Partitioning and file formats (Parquet, ORC)
                      Topic 4: Data Ingestion and Integration- Data movement and pipelines
                      • 1. Data flow orchestration
                        • 2. ETL/ELT workflows
                          - Batch and streaming ingestion
                          • 1. Data ingestion patterns in CDP
                            • 2. Apache NiFi / Kafka ingestion concepts
                              Topic 5: Data Processing and Transformation- SQL analytics engines
                              • 1. Hive / Impala query optimization
                                • 2. Data transformation best practices
                                  - Spark processing
                                  • 1. Spark SQL and DataFrames
                                    • 2. Distributed processing concepts

                                      Cloudera CDP Data Engineer - Certification Sample Questions:

                                      Question #1

                                      What is the best practice for handling DAG dependencies in Apache Airflow when one DAG's output is another DAG's input?

                                      • A. Use the TriggerDagRunOperator to trigger one DAG from another upon completion.
                                      • B. Manually trigger the dependent DAG after the first DAG completes.
                                      • C. Directly call one DAG from another using the PythonOperator.
                                      • D. Use the ExternalTaskSensor to wait for a task in another DAG to complete.
                                      Reveal Solution  Discussion  0

                                      Correct Answer: D  🗳️

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                                      Question #2

                                      A data engineer needs to query a table stored in Apache Hive using SparkSQL. Which of the following commands correctly retrieves data from a Hive table named 'sales data'?

                                      • A.
                                      • B.
                                      • C.
                                      • D.
                                      Reveal Solution  Discussion  0

                                      Correct Answer: D  🗳️

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                                      Question #3

                                      You're debugging a slow-running Spark job writing a large Iceberg table. Which optimization techniques could improve performance? (Choose three.

                                      • A. Using the Z-Order clustering option in Iceberg
                                      • B. Filtering data as early as possible in the Spark transformation pipeline
                                      • C. Converting the DataFrame to RDD for Iceberg writes
                                      • D. Disabling Spark's adaptive query execution
                                      • E. Repartitioning the DataFrame before writing to Iceberg
                                      Reveal Solution  Discussion  0

                                      Correct Answer: A,B,E  🗳️

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                                      Question #4

                                      What are the potential consequences of persisting all intermediate RDDs in Spark with the MEMORY ONLY storage level?

                                      • A. No impact on the application, as Spark manages memory efficiently
                                      • B. Increased risk of application crashes due to memory exhaustion
                                      • C. All intermediate data will be automatically persisted to disk
                                      • D. Improved performance due to faster data access
                                      Reveal Solution  Discussion  0

                                      Correct Answer: B  🗳️

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                                      Question #5

                                      A data engineer is deploying a Spark application on a Kubernetes cluster. To minimize downtime during updates, which Kubernetes deployment strategy should be considered?

                                      • A. Pod Autoscaling
                                      • B. Rolling Updates
                                      • C. Persistent Volumes
                                      • D. Node Affinity
                                      Reveal Solution  Discussion  0

                                      Correct Answer: B  🗳️

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